{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Analysis/logSTATA/003_balanceTest.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 6 Nov 2021, 19:49:41
{txt}
{com}. 
. 
. *******************************************************************************
. /*                                   RENEWABLES_VOTING (URPELAINEN & ZHANG)                              */
. *******************************************************************************
. 
. /* 
> 
> File Name:      003_balanceTest.do
> 
> By:                             Alice Tianbo Zhang (alice.tianbo.zhang@gmail.com)
> 
> Last Edited:    10/11/2021
> 
> Purpose:                1. Summary statistics of votes wind panel and election district panel
>                                 2. Exploratory data analysis of main variables
>                                 3. Conduct balance test using ACS panel
> 
> Data Used:      votes_wind_panel.dta
>                                 election_district_panel.dta     
>                                 ACS_panel_balanceTest_recodeVar.dta
>                                 
> */
. 
. ** Install packages
. *ssc install reghdfe
. *ssc install tabout
. *ssc install latab
. 
. *******************************************************************************
. /*                                               TABLE A1                                                                    */
. *******************************************************************************
. cd "$rootDir/$dataDir/Final"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Data/Final
{txt}
{com}. use votes_wind_panel.dta, clear
{txt}
{com}. 
. eststo clear
{txt}
{com}. eststo: estpost tabstat cum_count_turbine cum_capacity_turbine count_turbine capacity_turbine ///
>                                 mean_wp std_wp min_wp median_wp max_wp ///
>                                 pro_env anti_env ///
>                                 incumbvotesmajorpercent demvotesmajorpercent repvotesmajorpercent, ///
>                                 stat(mean sd min max count) col(stat)

{txt}Summary statistics: mean sd min max count
     for variables: cum_count_turbine cum_capacity_turbine count_turbine capacity_turbine mean_wp std_wp min_wp median_wp max_wp pro_env anti_env incumbvotesmajorpercent demvotesmajorpercent repvotesmajorpercent

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(count)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:cum_count_~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 71.39582}}}{space 1}{space 1}{ralign 9:{res:{sf: 408.1722}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:     5040}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:cum_capaci~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  45.5735}}}{space 1}{space 1}{ralign 9:{res:{sf: 209.7106}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:  2935.97}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:count_turb~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 6.333798}}}{space 1}{space 1}{ralign 9:{res:{sf: 35.61911}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      680}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:capacity_t~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 11.18447}}}{space 1}{space 1}{ralign 9:{res:{sf: 65.44925}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:  1320.95}}}{space 1}{space 1}{ralign 9:{res:{sf:     2864}}}{space 1}
{space 0}{space 0}{ralign 12:mean_wp}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.695802}}}{space 1}{space 1}{ralign 9:{res:{sf: .5240618}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:   4.0793}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:std_wp}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5198966}}}{space 1}{space 1}{ralign 9:{res:{sf: .3189492}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.527079}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:min_wp}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  1.12892}}}{space 1}{space 1}{ralign 9:{res:{sf: .3351696}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:median_wp}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.571429}}}{space 1}{space 1}{ralign 9:{res:{sf:  .591227}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:max_wp}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.560976}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.750051}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        7}}}{space 1}{space 1}{ralign 9:{res:{sf:     2870}}}{space 1}
{space 0}{space 0}{ralign 12:pro_env}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.38361}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.54201}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:     2868}}}{space 1}
{space 0}{space 0}{ralign 12:anti_env}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 40.15067}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.30665}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:     2868}}}{space 1}
{space 0}{space 0}{ralign 12:incumbvote~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 69.82382}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.83102}}}{space 1}{space 1}{ralign 9:{res:{sf:    38.98}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:     2602}}}{space 1}
{space 0}{space 0}{ralign 12:demvotesma~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 55.38949}}}{space 1}{space 1}{ralign 9:{res:{sf: 23.04784}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:     2868}}}{space 1}
{space 0}{space 0}{ralign 12:repvotesma~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 44.28038}}}{space 1}{space 1}{ralign 9:{res:{sf: 22.95421}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:     2867}}}{space 1}
({res}est1{txt} stored)

{com}. local summary_var cum_count_turbine cum_capacity_turbine count_turbine capacity_turbine mean_wp std_wp min_wp median_wp max_wp pro_env anti_env incumbvotesmajorpercent demvotesmajorpercent repvotesmajorpercent
{txt}
{com}. 
. ** Indent balance variables in table
. foreach v of varlist `summary_var' {c -(}
{txt}  2{com}.         label variable `v' `"\hspace{c -(}0.2cm{c )-} `: variable label `v''"'
{txt}  3{com}.         {c )-}       
{txt}
{com}. 
. ** Output table to LaTeX
. cd "$rootDir/$resultDir/Tables"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Tables
{txt}
{com}. esttab, cells("mean(fmt(%8.2f)) sd(fmt(%8.2f)) min(fmt(%8.0f)) max(fmt(%8.0f)) count(fmt(%8.0f))") coll(Mean SD Min Max Obs) ///
>                 varwidth(100) label wrap nostar unstack noobs nonote nomtitles nonumbers
{res}
{txt}{hline 165}
{txt}                                                                                                             Mean           SD          Min          Max          Obs
{txt}{hline 165}
{txt}\hspace{0.2cm} Cumulative number of wind turbines                                                   {res}        71.40       408.17            0         5040         2870{txt}
{txt}\hspace{0.2cm} Cumulative capacity of wind turbines (MW)                                            {res}        45.57       209.71            0         2936         2870{txt}
{txt}\hspace{0.2cm} Number of wind turbines                                                              {res}         6.33        35.62            0          680         2870{txt}
{txt}\hspace{0.2cm} Capacity of wind turbines (MW)                                                       {res}        11.18        65.45            0         1321         2864{txt}
{txt}\hspace{0.2cm} mean of zonal wind potential                                                         {res}         1.70         0.52            1            4         2870{txt}
{txt}\hspace{0.2cm} std of zonal wind potential                                                          {res}         0.52         0.32            0            2         2870{txt}
{txt}\hspace{0.2cm} min of zonal wind potential                                                          {res}         1.13         0.34            1            2         2870{txt}
{txt}\hspace{0.2cm} median of zonal wind potential                                                       {res}         1.57         0.59            1            4         2870{txt}
{txt}\hspace{0.2cm} max of zonal wind potential                                                          {res}         3.56         1.75            1            7         2870{txt}
{txt}\hspace{0.2cm} Pro-environment vote share                                                           {res}        56.38        40.54            0          100         2868{txt}
{txt}\hspace{0.2cm} Anti-environment vote share                                                          {res}        40.15        40.31            0          100         2868{txt}
{txt}\hspace{0.2cm} Incumbent vote share                                                                 {res}        69.82        13.83           39          100         2602{txt}
{txt}\hspace{0.2cm} Democratic candidate vote share                                                      {res}        55.39        23.05            0          100         2868{txt}
{txt}\hspace{0.2cm} Republican candidate vote share                                                      {res}        44.28        22.95            0          100         2867{txt}
{txt}{hline 165}

{com}. 
. esttab using TableA1.tex, booktabs replace ///
>                 refcat(cum_count_turbine "\emph{c -(}Wind Turbine Installation{c )-}" mean_wp "\emph{c -(}Zonal Wind Potential{c )-}" pro_env "\emph{c -(}Roll Call Vote Outcome {c )-}" incumbvotesmajorpercent "\emph{c -(}Election Outcome{c )-}" , nolabel) ///
>                 cells("mean(fmt(%8.2f)) sd(fmt(%8.2f)) min(fmt(%8.0f)) max(fmt(%8.0f)) count(fmt(%8.0f))") coll(Mean SD Min Max Obs) ///
>                 varwidth(100) label gaps nostar noobs nonote nomtitles nonumbers width(\hsize)
{res}{txt}(output written to {browse  `"TableA1.tex"'})

{com}. 
.                 
. *******************************************************************************
. /*                                               TABLE A2                                                                    */
. *******************************************************************************
. cd "$rootDir/$dataDir/Final"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Data/Final
{txt}
{com}. use election_district_panel.dta, clear
{txt}
{com}. 
. ** Label variables and create turnout variable
. gen turnout = (votes1 + votes2 + votes3 + votes4)/1000
{txt}
{com}. label variable incumbvotesmajorpercent "Incumbent candidates vote share (\%)"
{txt}
{com}. label variable demvotesmajorpercent "Democratic candidates vote share (\%)"
{txt}
{com}. label variable repvotesmajorpercent "Republican candidates vote share (\%)"
{txt}
{com}. label variable thirdvotestotalpercent "Third party candidates vote share (\%)" 
{txt}
{com}. label variable turnout "Total number of votes (thousand)"
{txt}
{com}. label variable demhouse "Number of Democrats in the House"
{txt}
{com}. label variable rephouse "Number of Republicans in the House"
{txt}
{com}. label variable indhouse "Number of Independents in the House"
{txt}
{com}. 
. tabstat turnout demvotesmajorpercent repvotesmajorpercent thirdvotestotalpercent ///
>                 incumbvotesmajorpercent demhouse rephouse indhouse, by(year) statistics(mean sd min max) nototal long

{txt}{lalign 8:year} {...}
   stats {...}
{c |}{...}
   turnout  demvot~t  repvot~t  thirdv~t  incumb~t  demhouse  rephouse  indhouse
{hline 18}{c +}{hline 80}
{lalign 8:2004} {...}
{ralign 8:mean} {...}
{c |}{...}
 {res} 263.2506  54.07366  45.92634  2.008571  71.50067  201.0139       233         1
{txt}{space 8} {...}
{ralign 8:sd} {...}
{c |}{...}
 {res} 57.74688   23.9887   23.9887  3.508557  12.66734  .2361125         0         0
{txt}{space 8} {...}
{ralign 8:min} {...}
{c |}{...}
 {res}  108.783         0         0         0      48.3       201       233         1
{txt}{space 8} {...}
{ralign 8:max} {...}
{c |}{...}
 {res}  407.291       100       100     22.23       100       205       233         1
{txt}{hline 18}{c +}{hline 80}
{lalign 8:2006} {...}
{ralign 8:mean} {...}
{c |}{...}
 {res} 191.2619  59.63516  40.36484         0  69.77866       232  203.3136   .010453
{txt}{space 8} {...}
{ralign 8:sd} {...}
{c |}{...}
 {res}  52.8825  22.07324  22.07324         0  14.93076         0  3.056449  .1018816
{txt}{space 8} {...}
{ralign 8:min} {...}
{c |}{...}
 {res}   58.883         0         0         0     38.98       232       203         0
{txt}{space 8} {...}
{ralign 8:max} {...}
{c |}{...}
 {res}   315.18       100       100         0       100       232       233         1
{txt}{hline 18}{c +}{hline 80}
{lalign 8:2008} {...}
{ralign 8:mean} {...}
{c |}{...}
 {res} 279.8084  61.02558  38.97442         0  70.71954       257  178.5263         0
{txt}{space 8} {...}
{ralign 8:sd} {...}
{c |}{...}
 {res}   60.159  21.69901  21.69901         0  14.24133         0  3.595308         0
{txt}{space 8} {...}
{ralign 8:min} {...}
{c |}{...}
 {res}  110.955         0         0         0      43.8       257       178         0
{txt}{space 8} {...}
{ralign 8:max} {...}
{c |}{...}
 {res}  419.698       100       100         0       100       257       203         0
{txt}{hline 18}{c +}{hline 80}
{lalign 8:2010} {...}
{ralign 8:mean} {...}
{c |}{...}
 {res} 202.3119  51.53596  48.46403         0  64.72292  194.1228       242         0
{txt}{space 8} {...}
{ralign 8:sd} {...}
{c |}{...}
 {res} 50.47153  19.23291  19.23291         0  12.95866  8.417098         0         0
{txt}{space 8} {...}
{ralign 8:min} {...}
{c |}{...}
 {res}        0         0         0         0     40.32       193       242         0
{txt}{space 8} {...}
{ralign 8:max} {...}
{c |}{...}
 {res}  331.258       100       100         0       100       257       242         0
{txt}{hline 18}{c BT}{hline 80}

{com}. 
. eststo clear 
{txt}
{com}. foreach y in 2004 2006 2008 2010{c -(}
{txt}  2{com}. 
.         estpost summarize turnout demvotesmajorpercent repvotesmajorpercent thirdvotestotalpercent ///
>                                                 incumbvotesmajorpercent demhouse rephouse indhouse if year == `y'
{txt}  3{com}.                 
.         est sto CY`y'
{txt}  4{com}. {c )-}

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:turnout}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 263.2506}}}{space 1}{space 1}{ralign 9:{res:{sf: 3334.703}}}{space 1}{space 1}{ralign 9:{res:{sf: 57.74688}}}{space 1}{space 1}{ralign 9:{res:{sf:  108.783}}}{space 1}{space 1}{ralign 9:{res:{sf:  407.291}}}{space 1}{space 1}{ralign 9:{res:{sf: 75552.92}}}{space 1}
{space 0}{space 0}{ralign 12:demvotesma~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 54.07366}}}{space 1}{space 1}{ralign 9:{res:{sf: 575.4576}}}{space 1}{space 1}{ralign 9:{res:{sf:  23.9887}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf: 15519.14}}}{space 1}
{space 0}{space 0}{ralign 12:repvotesma~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 45.92634}}}{space 1}{space 1}{ralign 9:{res:{sf: 575.4576}}}{space 1}{space 1}{ralign 9:{res:{sf:  23.9887}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf: 13180.86}}}{space 1}
{space 0}{space 0}{ralign 12:thirdvotes~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.008571}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.30997}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.508557}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:    22.23}}}{space 1}{space 1}{ralign 9:{res:{sf:   576.46}}}{space 1}
{space 0}{space 0}{ralign 12:incumbvote~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      268}}}{space 1}{space 1}{ralign 9:{res:{sf:      268}}}{space 1}{space 1}{ralign 9:{res:{sf: 71.50067}}}{space 1}{space 1}{ralign 9:{res:{sf: 160.4616}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.66734}}}{space 1}{space 1}{ralign 9:{res:{sf:     48.3}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf: 19162.18}}}{space 1}
{space 0}{space 0}{ralign 12:demhouse}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 201.0139}}}{space 1}{space 1}{ralign 9:{res:{sf: .0557491}}}{space 1}{space 1}{ralign 9:{res:{sf: .2361125}}}{space 1}{space 1}{ralign 9:{res:{sf:      201}}}{space 1}{space 1}{ralign 9:{res:{sf:      205}}}{space 1}{space 1}{ralign 9:{res:{sf:    57691}}}{space 1}
{space 0}{space 0}{ralign 12:rephouse}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      233}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      233}}}{space 1}{space 1}{ralign 9:{res:{sf:      233}}}{space 1}{space 1}{ralign 9:{res:{sf:    66871}}}{space 1}
{space 0}{space 0}{ralign 12:indhouse}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:turnout}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 191.2619}}}{space 1}{space 1}{ralign 9:{res:{sf: 2796.558}}}{space 1}{space 1}{ralign 9:{res:{sf:  52.8825}}}{space 1}{space 1}{ralign 9:{res:{sf:   58.883}}}{space 1}{space 1}{ralign 9:{res:{sf:   315.18}}}{space 1}{space 1}{ralign 9:{res:{sf: 54892.17}}}{space 1}
{space 0}{space 0}{ralign 12:demvotesma~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 59.63516}}}{space 1}{space 1}{ralign 9:{res:{sf: 487.2281}}}{space 1}{space 1}{ralign 9:{res:{sf: 22.07324}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf: 17115.29}}}{space 1}
{space 0}{space 0}{ralign 12:repvotesma~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.36484}}}{space 1}{space 1}{ralign 9:{res:{sf: 487.2281}}}{space 1}{space 1}{ralign 9:{res:{sf: 22.07324}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf: 11584.71}}}{space 1}
{space 0}{space 0}{ralign 12:thirdvotes~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:incumbvote~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      261}}}{space 1}{space 1}{ralign 9:{res:{sf:      261}}}{space 1}{space 1}{ralign 9:{res:{sf: 69.77866}}}{space 1}{space 1}{ralign 9:{res:{sf: 222.9275}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.93076}}}{space 1}{space 1}{ralign 9:{res:{sf:    38.98}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf: 18212.23}}}{space 1}
{space 0}{space 0}{ralign 12:demhouse}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      287}}}{space 1}{space 1}{ralign 9:{res:{sf:      232}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:      232}}}{space 1}{space 1}{ralign 9:{res:{sf:      232}}}{space 1}{space 1}{ralign 9:{res:{sf:    66584}}}{space 1}
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{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
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{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
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{com}. 
. esttab CY2004 CY2006 CY2008 CY2010, main(mean %8.2f) aux(sd %8.2f) varwidth(30) ///
>                                                                                    nostar nonote nonumber label mtitles("2004" "2006" "2008" "2010") width(\hsize)              
{res}
{txt}{hline 82}
{txt}                                       2004         2006         2008         2010
{txt}{hline 82}
{txt}Total number of votes (thous~){res}       263.25       191.26       279.81       202.31{txt}
                              {res} {ralign 12:{txt:(}57.75{txt:)}} {ralign 12:{txt:(}52.88{txt:)}} {ralign 12:{txt:(}60.16{txt:)}} {ralign 12:{txt:(}50.47{txt:)}}{txt}

{txt}Democratic candidates vote s~e{res}        54.07        59.64        61.03        51.54{txt}
                              {res} {ralign 12:{txt:(}23.99{txt:)}} {ralign 12:{txt:(}22.07{txt:)}} {ralign 12:{txt:(}21.70{txt:)}} {ralign 12:{txt:(}19.23{txt:)}}{txt}

{txt}Republican candidates vote s~e{res}        45.93        40.36        38.97        48.46{txt}
                              {res} {ralign 12:{txt:(}23.99{txt:)}} {ralign 12:{txt:(}22.07{txt:)}} {ralign 12:{txt:(}21.70{txt:)}} {ralign 12:{txt:(}19.23{txt:)}}{txt}

{txt}Third party candidates vote ~r{res}         2.01         0.00         0.00         0.00{txt}
                              {res} {ralign 12:{txt:(}3.51{txt:)}} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}0.00{txt:)}}{txt}

{txt}Incumbent candidates vote sh~ {res}        71.50        69.78        70.72        64.72{txt}
                              {res} {ralign 12:{txt:(}12.67{txt:)}} {ralign 12:{txt:(}14.93{txt:)}} {ralign 12:{txt:(}14.24{txt:)}} {ralign 12:{txt:(}12.96{txt:)}}{txt}

{txt}Number of Democrats in the H~e{res}       201.01       232.00       257.00       194.12{txt}
                              {res} {ralign 12:{txt:(}0.24{txt:)}} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}8.42{txt:)}}{txt}

{txt}Number of Republicans in the~u{res}       233.00       203.31       178.53       242.00{txt}
                              {res} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}3.06{txt:)}} {ralign 12:{txt:(}3.60{txt:)}} {ralign 12:{txt:(}0.00{txt:)}}{txt}

{txt}Number of Independents in th~o{res}         1.00         0.01         0.00         0.00{txt}
                              {res} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}0.10{txt:)}} {ralign 12:{txt:(}0.00{txt:)}} {ralign 12:{txt:(}0.00{txt:)}}{txt}
{txt}{hline 82}
{txt}Observations                  {res}          287          287          285          285{txt}
{txt}{hline 82}

{com}.                                                                    
. ** Output table to LaTeX
. cd "$rootDir/$resultDir/Tables"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Tables
{txt}
{com}. esttab CY2004 CY2006 CY2008 CY2010 using TableA2.tex, booktabs replace ///
>                                                                                   main(mean %8.2f) aux(sd %8.2f) stats(N, labels("Districts") fmt(0)) ///
>                                                                                   varwidth(30) gaps nostar nonote nonumber label ///
>                                                                                   mtitles("2004" "2006" "2008" "2010") width(\hsize)            
{res}{txt}(output written to {browse  `"TableA2.tex"'})

{com}.                 
.                 
. *******************************************************************************
. /*                                                FIGURE 2                                                                   */
. *******************************************************************************
. cd "$rootDir/$dataDir/Final"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Data/Final
{txt}
{com}. use votes_wind_panel.dta, clear
{txt}
{com}. 
. *----------------------- Growth of wind power by year  ----------------------*
. capture drop mean_count mean_capacity
{txt}
{com}. bysort year: egen mean_count = mean(cum_count_turbine)
{txt}
{com}. bysort year: egen mean_capacity = mean(cum_capacity_turbine)
{txt}
{com}. 
. graph twoway scatter mean_capa year, scheme(vg_s1c) graphregion(color(white)) ytitle("Mean Wind Capacity by District (MW)") yscale(range(0 120)) ylabel(0(20)120)
{res}{txt}
{com}. 
. cd "$rootDir/$graphDir"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Figures
{txt}
{com}. graph export fg2a.pdf, replace
{txt}(file /Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Figures/fg2a.pdf written in PDF format)

{com}. 
. graph twoway scatter mean_count year, scheme(vg_s1c) graphregion(color(white)) ytitle("Mean Wind Count by District") yscale(range(0 120)) ylabel(0(20)120)
{res}{txt}
{com}. cd "$rootDir/$graphDir"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Figures
{txt}
{com}. graph export fg2b.pdf, replace
{txt}(file /Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Figures/fg2b.pdf written in PDF format)

{com}. 
. *******************************************************************************
. /*                                              TABLE 1                                                              */
. *******************************************************************************
. cd "$rootDir/$dataDir/Final"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Data/Final
{txt}
{com}. use votes_wind_panel.dta, clear
{txt}
{com}. 
. ** Create dummy for treatment
. egen dtreat = max(cum_count_turbine > 0 | cum_capacity_turbine > 0), by(state district)
{txt}
{com}. 
. ** Summary of observations
. unique year 
{txt}Number of unique values of year is  {res}10
{txt}Number of records is  {res}2870
{txt}
{com}. unique state
{txt}Number of unique values of state is  {res}25
{txt}Number of records is  {res}2870
{txt}
{com}. unique panelID
{txt}Number of unique values of panelID is  {res}287
{txt}Number of records is  {res}2870
{txt}
{com}. bysort dtreat: distinct panelID

{txt}{hline}
-> dtreat = 0

{col 10}{c |}        Observations
{col 10}{c |}      total   distinct
{hline 9}{c +}{hline 22}
 panelID {c |}  {res}     1830        183

{txt}{hline}
-> dtreat = 1

{col 10}{c |}        Observations
{col 10}{c |}      total   distinct
{hline 9}{c +}{hline 22}
 panelID {c |}  {res}     1040        104
{txt}
{com}. bysort dtreat: count 

{txt}{hline}
-> dtreat = 0
  {res}1,830
{txt}{hline}
-> dtreat = 1
  {res}1,040
{txt}
{com}. 
. 
.    
. *******************************************************************************
. /*                                              TABLE A3                                                             */
. *******************************************************************************
. cd "$rootDir/$dataDir/Final"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Data/Final
{txt}
{com}. use ACS_panel_balanceTest_recodeVar.dta, clear
{txt}
{com}. 
. ** Create list of local variables for balance test
. local balance_var pop white foreign male old median_income average_income home_median non_poor edu_male edu_female prim_prod manu hours
{txt}
{com}. 
. 
. *------------------------ Balance Test: Table -------------------------*
. eststo clear
{txt}
{com}. eststo: estpost sum `balance_var' if dtreat == 1

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{space 0}{space 0}{ralign 12:edu_female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf: .2353412}}}{space 1}{space 1}{ralign 9:{res:{sf: .0028376}}}{space 1}{space 1}{ralign 9:{res:{sf: .0532693}}}{space 1}{space 1}{ralign 9:{res:{sf: .0970284}}}{space 1}{space 1}{ralign 9:{res:{sf: .3815072}}}{space 1}{space 1}{ralign 9:{res:{sf: 149.9123}}}{space 1}
{space 0}{space 0}{ralign 12:prim_prod}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf: .0247365}}}{space 1}{space 1}{ralign 9:{res:{sf: .0005603}}}{space 1}{space 1}{ralign 9:{res:{sf: .0236711}}}{space 1}{space 1}{ralign 9:{res:{sf: .0001741}}}{space 1}{space 1}{ralign 9:{res:{sf: .1345447}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.75715}}}{space 1}
{space 0}{space 0}{ralign 12:manu}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf: .1273039}}}{space 1}{space 1}{ralign 9:{res:{sf: .0026588}}}{space 1}{space 1}{ralign 9:{res:{sf: .0515635}}}{space 1}{space 1}{ralign 9:{res:{sf: .0240565}}}{space 1}{space 1}{ralign 9:{res:{sf: .3033075}}}{space 1}{space 1}{ralign 9:{res:{sf: 81.09261}}}{space 1}
{space 0}{space 0}{ralign 12:hours}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf:      637}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.43265}}}{space 1}{space 1}{ralign 9:{res:{sf: .7110076}}}{space 1}{space 1}{ralign 9:{res:{sf: .8432127}}}{space 1}{space 1}{ralign 9:{res:{sf:     35.7}}}{space 1}{space 1}{ralign 9:{res:{sf:     40.9}}}{space 1}{space 1}{ralign 9:{res:{sf:  24481.6}}}{space 1}
({res}est1{txt} stored)

{com}. eststo: estpost sum `balance_var' if dtreat == 0

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: 682.4373}}}{space 1}{space 1}{ralign 9:{res:{sf: 3594.671}}}{space 1}{space 1}{ralign 9:{res:{sf: 59.95558}}}{space 1}{space 1}{ralign 9:{res:{sf:  506.036}}}{space 1}{space 1}{ralign 9:{res:{sf: 1061.221}}}{space 1}{space 1}{ralign 9:{res:{sf:   936304}}}{space 1}
{space 0}{space 0}{ralign 12:white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .6959628}}}{space 1}{space 1}{ralign 9:{res:{sf: .0385037}}}{space 1}{space 1}{ralign 9:{res:{sf: .1962235}}}{space 1}{space 1}{ralign 9:{res:{sf: .1161201}}}{space 1}{space 1}{ralign 9:{res:{sf: .9646632}}}{space 1}{space 1}{ralign 9:{res:{sf: 954.8609}}}{space 1}
{space 0}{space 0}{ralign 12:foreign}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .0134941}}}{space 1}{space 1}{ralign 9:{res:{sf: .0001439}}}{space 1}{space 1}{ralign 9:{res:{sf: .0119943}}}{space 1}{space 1}{ralign 9:{res:{sf: .0025327}}}{space 1}{space 1}{ralign 9:{res:{sf: .1171315}}}{space 1}{space 1}{ralign 9:{res:{sf: 18.51387}}}{space 1}
{space 0}{space 0}{ralign 12:male}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .4908959}}}{space 1}{space 1}{ralign 9:{res:{sf: .0001464}}}{space 1}{space 1}{ralign 9:{res:{sf: .0120976}}}{space 1}{space 1}{ralign 9:{res:{sf: .4495972}}}{space 1}{space 1}{ralign 9:{res:{sf: .5449896}}}{space 1}{space 1}{ralign 9:{res:{sf: 673.5091}}}{space 1}
{space 0}{space 0}{ralign 12:old}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .1237776}}}{space 1}{space 1}{ralign 9:{res:{sf: .0005625}}}{space 1}{space 1}{ralign 9:{res:{sf: .0237167}}}{space 1}{space 1}{ralign 9:{res:{sf: .0529949}}}{space 1}{space 1}{ralign 9:{res:{sf: .1943409}}}{space 1}{space 1}{ralign 9:{res:{sf: 169.8229}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:   54.339}}}{space 1}{space 1}{ralign 9:{res:{sf: 230.8289}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.19306}}}{space 1}{space 1}{ralign 9:{res:{sf:   19.018}}}{space 1}{space 1}{ralign 9:{res:{sf:   99.811}}}{space 1}{space 1}{ralign 9:{res:{sf: 74553.11}}}{space 1}
{space 0}{space 0}{ralign 12:average_in~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: 27.73036}}}{space 1}{space 1}{ralign 9:{res:{sf: 72.25349}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.500205}}}{space 1}{space 1}{ralign 9:{res:{sf:   10.786}}}{space 1}{space 1}{ralign 9:{res:{sf:    77.09}}}{space 1}{space 1}{ralign 9:{res:{sf: 38046.05}}}{space 1}
{space 0}{space 0}{ralign 12:home_median}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: 904.8637}}}{space 1}{space 1}{ralign 9:{res:{sf: 63349.13}}}{space 1}{space 1}{ralign 9:{res:{sf: 251.6925}}}{space 1}{space 1}{ralign 9:{res:{sf:      457}}}{space 1}{space 1}{ralign 9:{res:{sf:     1716}}}{space 1}{space 1}{ralign 9:{res:{sf:  1241473}}}{space 1}
{space 0}{space 0}{ralign 12:non_poor}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .8429041}}}{space 1}{space 1}{ralign 9:{res:{sf: .0041658}}}{space 1}{space 1}{ralign 9:{res:{sf:  .064543}}}{space 1}{space 1}{ralign 9:{res:{sf: .5793203}}}{space 1}{space 1}{ralign 9:{res:{sf: .9687206}}}{space 1}{space 1}{ralign 9:{res:{sf: 1156.464}}}{space 1}
{space 0}{space 0}{ralign 12:edu_male}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .2404924}}}{space 1}{space 1}{ralign 9:{res:{sf: .0069838}}}{space 1}{space 1}{ralign 9:{res:{sf: .0835688}}}{space 1}{space 1}{ralign 9:{res:{sf: .0586258}}}{space 1}{space 1}{ralign 9:{res:{sf: .5557974}}}{space 1}{space 1}{ralign 9:{res:{sf: 329.9556}}}{space 1}
{space 0}{space 0}{ralign 12:edu_female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .2506881}}}{space 1}{space 1}{ralign 9:{res:{sf: .0059429}}}{space 1}{space 1}{ralign 9:{res:{sf: .0770904}}}{space 1}{space 1}{ralign 9:{res:{sf: .0704394}}}{space 1}{space 1}{ralign 9:{res:{sf: .5640553}}}{space 1}{space 1}{ralign 9:{res:{sf: 343.9441}}}{space 1}
{space 0}{space 0}{ralign 12:prim_prod}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .0118471}}}{space 1}{space 1}{ralign 9:{res:{sf: .0005186}}}{space 1}{space 1}{ralign 9:{res:{sf: .0227722}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .2557325}}}{space 1}{space 1}{ralign 9:{res:{sf: 16.25426}}}{space 1}
{space 0}{space 0}{ralign 12:manu}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: .1122874}}}{space 1}{space 1}{ralign 9:{res:{sf: .0023924}}}{space 1}{space 1}{ralign 9:{res:{sf: .0489124}}}{space 1}{space 1}{ralign 9:{res:{sf:  .023137}}}{space 1}{space 1}{ralign 9:{res:{sf: .2652171}}}{space 1}{space 1}{ralign 9:{res:{sf: 154.0583}}}{space 1}
{space 0}{space 0}{ralign 12:hours}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf:     1372}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.64665}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.083541}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.040933}}}{space 1}{space 1}{ralign 9:{res:{sf:     35.6}}}{space 1}{space 1}{ralign 9:{res:{sf:     44.5}}}{space 1}{space 1}{ralign 9:{res:{sf:  53023.2}}}{space 1}
({res}est2{txt} stored)

{com}. eststo: estpost sum `balance_var'

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:pop}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: 685.3529}}}{space 1}{space 1}{ralign 9:{res:{sf: 3643.632}}}{space 1}{space 1}{ralign 9:{res:{sf: 60.36251}}}{space 1}{space 1}{ralign 9:{res:{sf:  506.036}}}{space 1}{space 1}{ralign 9:{res:{sf: 1061.221}}}{space 1}{space 1}{ralign 9:{res:{sf:  1376874}}}{space 1}
{space 0}{space 0}{ralign 12:white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: .7401328}}}{space 1}{space 1}{ralign 9:{res:{sf: .0351434}}}{space 1}{space 1}{ralign 9:{res:{sf: .1874657}}}{space 1}{space 1}{ralign 9:{res:{sf: .1161201}}}{space 1}{space 1}{ralign 9:{res:{sf: .9702255}}}{space 1}{space 1}{ralign 9:{res:{sf: 1486.927}}}{space 1}
{space 0}{space 0}{ralign 12:foreign}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: .0125251}}}{space 1}{space 1}{ralign 9:{res:{sf: .0001185}}}{space 1}{space 1}{ralign 9:{res:{sf:  .010884}}}{space 1}{space 1}{ralign 9:{res:{sf: .0025327}}}{space 1}{space 1}{ralign 9:{res:{sf: .1171315}}}{space 1}{space 1}{ralign 9:{res:{sf: 25.16298}}}{space 1}
{space 0}{space 0}{ralign 12:male}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:  .491947}}}{space 1}{space 1}{ralign 9:{res:{sf: .0001242}}}{space 1}{space 1}{ralign 9:{res:{sf:  .011145}}}{space 1}{space 1}{ralign 9:{res:{sf: .4495972}}}{space 1}{space 1}{ralign 9:{res:{sf: .5449896}}}{space 1}{space 1}{ralign 9:{res:{sf: 988.3216}}}{space 1}
{space 0}{space 0}{ralign 12:old}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: .1272234}}}{space 1}{space 1}{ralign 9:{res:{sf: .0006001}}}{space 1}{space 1}{ralign 9:{res:{sf: .0244972}}}{space 1}{space 1}{ralign 9:{res:{sf: .0529949}}}{space 1}{space 1}{ralign 9:{res:{sf: .1962764}}}{space 1}{space 1}{ralign 9:{res:{sf: 255.5917}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: 53.45172}}}{space 1}{space 1}{ralign 9:{res:{sf: 194.5864}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.94942}}}{space 1}{space 1}{ralign 9:{res:{sf:   19.018}}}{space 1}{space 1}{ralign 9:{res:{sf:   99.811}}}{space 1}{space 1}{ralign 9:{res:{sf: 107384.5}}}{space 1}
{space 0}{space 0}{ralign 12:average_in~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2007}}}{space 1}{space 1}{ralign 9:{res:{sf:     2007}}}{space 1}{space 1}{ralign 9:{res:{sf: 27.10084}}}{space 1}{space 1}{ralign 9:{res:{sf: 57.14027}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.559119}}}{space 1}{space 1}{ralign 9:{res:{sf:   10.786}}}{space 1}{space 1}{ralign 9:{res:{sf:    77.09}}}{space 1}{space 1}{ralign 9:{res:{sf: 54391.38}}}{space 1}
{space 0}{space 0}{ralign 12:home_median}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: 861.6695}}}{space 1}{space 1}{ralign 9:{res:{sf: 59292.64}}}{space 1}{space 1}{ralign 9:{res:{sf: 243.5008}}}{space 1}{space 1}{ralign 9:{res:{sf:      457}}}{space 1}{space 1}{ralign 9:{res:{sf:     1716}}}{space 1}{space 1}{ralign 9:{res:{sf:  1731094}}}{space 1}
{space 0}{space 0}{ralign 12:non_poor}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: .8458841}}}{space 1}{space 1}{ralign 9:{res:{sf: .0034787}}}{space 1}{space 1}{ralign 9:{res:{sf: .0589804}}}{space 1}{space 1}{ralign 9:{res:{sf: .5793203}}}{space 1}{space 1}{ralign 9:{res:{sf: .9687206}}}{space 1}{space 1}{ralign 9:{res:{sf: 1699.381}}}{space 1}
{space 0}{space 0}{ralign 12:edu_male}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: .2345535}}}{space 1}{space 1}{ralign 9:{res:{sf: .0058165}}}{space 1}{space 1}{ralign 9:{res:{sf: .0762658}}}{space 1}{space 1}{ralign 9:{res:{sf: .0586258}}}{space 1}{space 1}{ralign 9:{res:{sf: .5557974}}}{space 1}{space 1}{ralign 9:{res:{sf: 471.2181}}}{space 1}
{space 0}{space 0}{ralign 12:edu_female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:  .245822}}}{space 1}{space 1}{ralign 9:{res:{sf: .0050074}}}{space 1}{space 1}{ralign 9:{res:{sf: .0707633}}}{space 1}{space 1}{ralign 9:{res:{sf: .0704394}}}{space 1}{space 1}{ralign 9:{res:{sf: .5640553}}}{space 1}{space 1}{ralign 9:{res:{sf: 493.8564}}}{space 1}
{space 0}{space 0}{ralign 12:prim_prod}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:  .015934}}}{space 1}{space 1}{ralign 9:{res:{sf: .0005675}}}{space 1}{space 1}{ralign 9:{res:{sf: .0238229}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .2557325}}}{space 1}{space 1}{ralign 9:{res:{sf:  32.0114}}}{space 1}
{space 0}{space 0}{ralign 12:manu}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf: .1170487}}}{space 1}{space 1}{ralign 9:{res:{sf: .0025245}}}{space 1}{space 1}{ralign 9:{res:{sf:  .050244}}}{space 1}{space 1}{ralign 9:{res:{sf:  .023137}}}{space 1}{space 1}{ralign 9:{res:{sf: .3033075}}}{space 1}{space 1}{ralign 9:{res:{sf: 235.1509}}}{space 1}
{space 0}{space 0}{ralign 12:hours}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:     2009}}}{space 1}{space 1}{ralign 9:{res:{sf:  38.5788}}}{space 1}{space 1}{ralign 9:{res:{sf: .9749287}}}{space 1}{space 1}{ralign 9:{res:{sf: .9873848}}}{space 1}{space 1}{ralign 9:{res:{sf:     35.6}}}{space 1}{space 1}{ralign 9:{res:{sf:     44.5}}}{space 1}{space 1}{ralign 9:{res:{sf:  77504.8}}}{space 1}
({res}est3{txt} stored)

{com}. 
. ** Indent balance variables in table
. foreach v of varlist `balance_var' {c -(}
{txt}  2{com}.         label variable `v' `"\hspace{c -(}0.2cm{c )-} `: variable label `v''"'
{txt}  3{com}.         {c )-}
{txt}
{com}. 
. ** Output table to LaTex
. cd "$rootDir/$resultDir/Tables"
{res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Results/Tables
{txt}
{com}. esttab, main(mean) aux(sd) stats(N, labels("Observations") fmt(0)) ///
>                 label wrap nostar unstack noobs nonumbers nonote width(\hsize) ///
>                 mtitles("With Wind" "Without Wind" "Overall") 
{res}
{txt}{hline 59}
{txt}                        With Wind Without Wind      Overall
{txt}{hline 59}
{txt}\hspace{0.2cm} Total{res}        691.6        682.4        685.4{txt}
population (thousa~){res} {ralign 12:{txt:(}60.80{txt:)}} {ralign 12:{txt:(}59.96{txt:)}} {ralign 12:{txt:(}60.36{txt:)}}{txt}

{txt}\hspace{0.2cm} White{res}        0.835        0.696        0.740{txt}
(\%)                {res} {ralign 12:{txt:(}0.121{txt:)}} {ralign 12:{txt:(}0.196{txt:)}} {ralign 12:{txt:(}0.187{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}       0.0104       0.0135       0.0125{txt}
Native, born ..S. ~){res} {ralign 12:{txt:(}0.00758{txt:)}} {ralign 12:{txt:(}0.0120{txt:)}} {ralign 12:{txt:(}0.0109{txt:)}}{txt}

{txt}\hspace{0.2cm} Male {res}        0.494        0.491        0.492{txt}
(\%)                {res} {ralign 12:{txt:(}0.00832{txt:)}} {ralign 12:{txt:(}0.0121{txt:)}} {ralign 12:{txt:(}0.0111{txt:)}}{txt}

{txt}\hspace{0.2cm} Older{res}        0.135        0.124        0.127{txt}
than 65 (\%)        {res} {ralign 12:{txt:(}0.0245{txt:)}} {ralign 12:{txt:(}0.0237{txt:)}} {ralign 12:{txt:(}0.0245{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}        51.54        54.34        53.45{txt}
Median household i~o{res} {ralign 12:{txt:(}10.56{txt:)}} {ralign 12:{txt:(}15.19{txt:)}} {ralign 12:{txt:(}13.95{txt:)}}{txt}

{txt}\hspace{0.2cm} Per  {res}        25.74        27.73        27.10{txt}
capita income ~1000){res} {ralign 12:{txt:(}4.673{txt:)}} {ralign 12:{txt:(}8.500{txt:)}} {ralign 12:{txt:(}7.559{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}        768.6        904.9        861.7{txt}
Median gross rent ~){res} {ralign 12:{txt:(}194.8{txt:)}} {ralign 12:{txt:(}251.7{txt:)}} {ralign 12:{txt:(}243.5{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}        0.852        0.843        0.846{txt}
Income at or above~l{res} {ralign 12:{txt:(}0.0441{txt:)}} {ralign 12:{txt:(}0.0645{txt:)}} {ralign 12:{txt:(}0.0590{txt:)}}{txt}

{txt}\hspace{0.2cm} Male {res}        0.222        0.240        0.235{txt}
with associate deg~e{res} {ralign 12:{txt:(}0.0554{txt:)}} {ralign 12:{txt:(}0.0836{txt:)}} {ralign 12:{txt:(}0.0763{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}        0.235        0.251        0.246{txt}
Female with associ~n{res} {ralign 12:{txt:(}0.0533{txt:)}} {ralign 12:{txt:(}0.0771{txt:)}} {ralign 12:{txt:(}0.0708{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}       0.0247       0.0118       0.0159{txt}
Employment in agri~){res} {ralign 12:{txt:(}0.0237{txt:)}} {ralign 12:{txt:(}0.0228{txt:)}} {ralign 12:{txt:(}0.0238{txt:)}}{txt}

{txt}\hspace{0.2cm}      {res}        0.127        0.112        0.117{txt}
Employment in manu~){res} {ralign 12:{txt:(}0.0516{txt:)}} {ralign 12:{txt:(}0.0489{txt:)}} {ralign 12:{txt:(}0.0502{txt:)}}{txt}

{txt}\hspace{0.2cm} Mean {res}        38.43        38.65        38.58{txt}
hours worked        {res} {ralign 12:{txt:(}0.843{txt:)}} {ralign 12:{txt:(}1.041{txt:)}} {ralign 12:{txt:(}0.987{txt:)}}{txt}
{txt}{hline 59}
{txt}Observations        {res}          637         1372         2009{txt}
{txt}{hline 59}

{com}. 
. esttab using TableA3.tex, booktabs replace ///
>                 refcat(pop "\emph{c -(}Demographics{c )-}" median_income "\emph{c -(}Income \& Poverty{c )-}" edu_male "\emph{c -(}Education{c )-}" prim_prod "\emph{c -(}Employment{c )-}", nolabel) ///
>                 main(mean) aux(sd) stats(N, labels("Observations") fmt(0)) ///
>                 label gaps nostar noobs nonumbers nonote width(\hsize) ///
>                 mtitles("With Wind" "Without Wind" "Overall") 
{res}{txt}(output written to {browse  `"TableA3.tex"'})

{com}. 
.         
. *******************************************************************************
. /*                                              TABLES A4-A6                                                 */
. *******************************************************************************
. ** Create interaction and fixed effects
. gen t = year - 2005
{txt}
{com}. gen inter = t * mean_wp
{txt}
{com}. 
. egen fixed = group(state year)
{txt}
{com}. egen district_fixed = group(state district)
{txt}
{com}. 
. ** Balance regressions
. eststo clear
{txt}
{com}. local balance_var pop white foreign male old median_income average_income home_value home_median non_poor hours edu_male edu_female prim_prod manu
{txt}
{com}. 
. foreach var in `balance_var'{c -(}
{txt}  2{com}.         eststo: reghdfe `var' inter, absorb(fixed district_fixed) vce(cluster district_fixed) 
{txt}  3{com}.         
. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      5.82
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0165
{txt}{col 51}R-squared{col 67}= {res}    0.9577
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9451
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0126
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}   14.1456

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}         pop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} 1.849231{col 26}{space 2}  .766433{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .3406661{col 67}{space 3} 3.357796
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 675.9451{col 26}{space 2} 3.899155{col 37}{space 1}  173.36{col 46}{space 3}0.000{col 54}{space 4} 668.2704{col 67}{space 3} 683.6198
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est1{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      3.62
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0583
{txt}{col 51}R-squared{col 67}= {res}    0.9922
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9899
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0055
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0188

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       white{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-.0016206{col 26}{space 2} .0008523{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4}-.0032982{col 67}{space 3}  .000057
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7483774{col 26}{space 2} .0043361{col 37}{space 1}  172.59{col 46}{space 3}0.000{col 54}{space 4} .7398427{col 67}{space 3} .7569122
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est2{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      2.35
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1264
{txt}{col 51}R-squared{col 67}= {res}    0.9730
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9649
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0037
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0020

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     foreign{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} .0001444{col 26}{space 2} .0000942{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4} -.000041{col 67}{space 3} .0003298
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0117905{col 26}{space 2} .0004793{col 37}{space 1}   24.60{col 46}{space 3}0.000{col 54}{space 4} .0108472{col 67}{space 3} .0127338
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est3{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.57
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4515
{txt}{col 51}R-squared{col 67}= {res}    0.8936
{txt}{col 51}Adj R-squared{col 67}= {res}    0.8618
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0005
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0041

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        male{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} .0001064{col 26}{space 2} .0001411{col 37}{space 1}    0.75{col 46}{space 3}0.452{col 54}{space 4}-.0001714{col 67}{space 3} .0003842
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4914058{col 26}{space 2} .0007179{col 37}{space 1}  684.47{col 46}{space 3}0.000{col 54}{space 4} .4899927{col 67}{space 3} .4928189
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est4{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      2.44
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.1194
{txt}{col 51}R-squared{col 67}= {res}    0.9767
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9697
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0039
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0043

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}         old{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} .0003071{col 26}{space 2} .0001966{col 37}{space 1}    1.56{col 46}{space 3}0.119{col 54}{space 4}-.0000798{col 67}{space 3}  .000694
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1256611{col 26}{space 2} .0010001{col 37}{space 1}  125.65{col 46}{space 3}0.000{col 54}{space 4} .1236927{col 67}{space 3} .1276296
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est5{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.12
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.7307
{txt}{col 51}R-squared{col 67}= {res}    0.9918
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9894
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0002
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    1.4389

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}median_inc~e{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} -.020661{col 26}{space 2} .0599594{col 37}{space 1}   -0.34{col 46}{space 3}0.731{col 54}{space 4}-.1386787{col 67}{space 3} .0973568
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 53.55683{col 26}{space 2} .3050379{col 37}{space 1}  175.57{col 46}{space 3}0.000{col 54}{space 4} 52.95643{col 67}{space 3} 54.15724
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est6{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 3 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,007
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      1.02
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3144
{txt}{col 51}R-squared{col 67}= {res}    0.9925
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9902
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0013
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.7486

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}average_in~e{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-.0311974{col 26}{space 2}  .030954{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4} -.092124{col 67}{space 3} .0297292
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 27.25956{col 26}{space 2} .1574797{col 37}{space 1}  173.10{col 46}{space 3}0.000{col 54}{space 4} 26.94959{col 67}{space 3} 27.56952
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est7{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      5.17
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0238
{txt}{col 51}R-squared{col 67}= {res}    0.9930
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9910
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0142
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}   16.2747

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  home_value{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-2.258027{col 26}{space 2} .9934633{col 37}{space 1}   -2.27{col 46}{space 3}0.024{col 54}{space 4}-4.213454{col 67}{space 3}-.3025994
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 281.1931{col 26}{space 2}  5.05415{col 37}{space 1}   55.64{col 46}{space 3}0.000{col 54}{space 4} 271.2451{col 67}{space 3} 291.1412
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est8{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      5.26
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0225
{txt}{col 51}R-squared{col 67}= {res}    0.9928
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9906
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0119
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}   23.5489

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} home_median{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-2.993878{col 26}{space 2}  1.30533{col 37}{space 1}   -2.29{col 46}{space 3}0.023{col 54}{space 4} -5.56315{col 67}{space 3}-.4246051
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 876.9006{col 26}{space 2} 6.640743{col 37}{space 1}  132.05{col 46}{space 3}0.000{col 54}{space 4} 863.8296{col 67}{space 3} 889.9715
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est9{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.05
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.8304
{txt}{col 51}R-squared{col 67}= {res}    0.9756
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9683
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0001
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0105

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    non_poor{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-.0001023{col 26}{space 2} .0004771{col 37}{space 1}   -0.21{col 46}{space 3}0.830{col 54}{space 4}-.0010413{col 67}{space 3} .0008367
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8464045{col 26}{space 2} .0024271{col 37}{space 1}  348.73{col 46}{space 3}0.000{col 54}{space 4} .8416273{col 67}{space 3} .8511817
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est10{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.93
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.3360
{txt}{col 51}R-squared{col 67}= {res}    0.9417
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9242
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0012
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.2718

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       hours{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} -.010858{col 26}{space 2} .0112669{col 37}{space 1}   -0.96{col 46}{space 3}0.336{col 54}{space 4}-.0330347{col 67}{space 3} .0113186
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 38.63403{col 26}{space 2} .0573195{col 37}{space 1}  674.01{col 46}{space 3}0.000{col 54}{space 4} 38.52121{col 67}{space 3} 38.74686
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est11{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.66
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4169
{txt}{col 51}R-squared{col 67}= {res}    0.9917
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9892
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0007
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0079

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    edu_male{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-.0002457{col 26}{space 2} .0003023{col 37}{space 1}   -0.81{col 46}{space 3}0.417{col 54}{space 4}-.0008407{col 67}{space 3} .0003492
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2358037{col 26}{space 2} .0015378{col 37}{space 1}  153.33{col 46}{space 3}0.000{col 54}{space 4} .2327768{col 67}{space 3} .2388307
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est12{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.68
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4094
{txt}{col 51}R-squared{col 67}= {res}    0.9907
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9879
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0006
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0078

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  edu_female{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2}-.0002157{col 26}{space 2} .0002611{col 37}{space 1}   -0.83{col 46}{space 3}0.409{col 54}{space 4}-.0007296{col 67}{space 3} .0002982
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2469193{col 26}{space 2} .0013282{col 37}{space 1}  185.91{col 46}{space 3}0.000{col 54}{space 4} .2443051{col 67}{space 3} .2495336
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est13{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      0.52
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.4734
{txt}{col 51}R-squared{col 67}= {res}    0.9862
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9821
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0012
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0032

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   prim_prod{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} -.000127{col 26}{space 2} .0001769{col 37}{space 1}   -0.72{col 46}{space 3}0.473{col 54}{space 4}-.0004751{col 67}{space 3} .0002212
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}   .01658{col 26}{space 2} .0008999{col 37}{space 1}   18.42{col 46}{space 3}0.000{col 54}{space 4} .0148088{col 67}{space 3} .0183512
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est14{txt} stored)
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 2 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     2,009
{txt}Absorbing 2 HDFE groups{col 51}F({res}   1{txt},{res}    286{txt}){col 67}= {res}      1.13
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.2893
{txt}{col 51}R-squared{col 67}= {res}    0.9836
{txt}{col 51}Adj R-squared{col 67}= {res}    0.9787
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0017
{txt}{col 1}Number of clusters ({res}district_fixed{txt}) {col 30}= {res}       287{txt}{col 51}Root MSE{col 67}= {res}    0.0073

{txt}{ralign 78:(Std. Err. adjusted for {res:287} clusters in district_fixed)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        manu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}inter {c |}{col 14}{res}{space 2} .0003488{col 26}{space 2} .0003286{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.0002979{col 67}{space 3} .0009956
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1152741{col 26}{space 2} .0016716{col 37}{space 1}   68.96{col 46}{space 3}0.000{col 54}{space 4} .1119839{col 67}{space 3} .1185643
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 16}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}    Absorbed FE{col 17}{c |} Categories{col 30} - Redundant{col 42}  = Num. Coefs{col 57}{c |}
{res}{col 1}{text}{hline 16}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}          fixed{col 17}{c |}{space 1}      175{col 30}{space 1}        0{col 42}{result}{space 1}      175{col 56}{text} {col 57}{c |}
{res}{col 1}{text} district_fixed{col 17}{c |}{space 1}      287{col 30}{space 1}      287{col 42}{result}{space 1}        0{col 56}{text}*{col 57}{c |}
{res}{col 1}{text}{hline 16}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
* = FE nested within cluster; treated as redundant for DoF computation
{res}{txt}({res}est15{txt} stored)

{com}. 
. ** Demographics
. esttab est1 est2 est3 est4 est5, ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") fmt(0 0)) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.10 ** 0.05 *** 0.01) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) ///
>                 mtitles("Pop" "White" "Foreign" "Male" ">65")
{res}
{txt}{hline 110}
{txt}                                        (1)             (2)             (3)             (4)             (5)   
{txt}                                        Pop           White         Foreign            Male             >65   
{txt}{hline 110}
{txt}Mean wind potential * time    {res}       1.8492**       -0.0016*         0.0001          0.0001          0.0003   {txt}
                              {res} {ralign 12:{txt:(}0.7664{txt:)}}    {ralign 12:{txt:(}0.0009{txt:)}}    {ralign 12:{txt:(}0.0001{txt:)}}    {ralign 12:{txt:(}0.0001{txt:)}}    {ralign 12:{txt:(}0.0002{txt:)}}   {txt}
{txt}{hline 110}
{txt}Observations                  {res}         2009            2009            2009            2009            2009   {txt}
{txt}Districts                     {res}          287             287             287             287             287   {txt}
{txt}{hline 110}
{txt}* p<0.10, ** p<0.05, *** p<0.01

{com}. 
. esttab est1 est2 est3 est4 est5 using TableA4.tex, booktabs replace ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") fmt(0 0)) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.10 ** 0.05 *** 0.01) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) ///
>                 mtitles("Pop" "White" "Foreign" "Male" "65+") width(\hsize)
{res}{txt}(output written to {browse  `"TableA4.tex"'})

{com}. 
. ** Income
. esttab est6 est7 est9 est10, ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.10 ** 0.05 *** 0.01) ///
>                 mtitles("Med Inc" "PC Inc" "Med Rent" "Abv Poverty")
{res}
{txt}{hline 94}
{txt}                                        (1)             (2)             (3)             (4)   
{txt}                                    Med Inc          PC Inc        Med Rent     Abv Poverty   
{txt}{hline 94}
{txt}Mean wind potential * time    {res}      -0.0207         -0.0312         -2.9939**       -0.0001   {txt}
                              {res} {ralign 12:{txt:(}0.0600{txt:)}}    {ralign 12:{txt:(}0.0310{txt:)}}    {ralign 12:{txt:(}1.3053{txt:)}}    {ralign 12:{txt:(}0.0005{txt:)}}   {txt}
{txt}{hline 94}
{txt}Observations                  {res}         2009            2007            2009            2009   {txt}
{txt}Districts                     {res}          287             287             287             287   {txt}
{txt}{hline 94}
{txt}* p<0.10, ** p<0.05, *** p<0.01

{com}. 
. esttab est6 est7 est9 est10 using TableA5.tex, booktabs replace ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.10 ** 0.05 *** 0.01) ///
>                 mtitles("Med Inc" "PC Inc" "Med Rent" "Abv Poverty") width(\hsize)
{res}{txt}(output written to {browse  `"TableA5.tex"'})

{com}. 
. 
. ** Education & Employment
. esttab est11 est12 est13 est14 est15, ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.10 ** 0.05 *** 0.01) ///
>                 mtitles("Avg Hrs" "Male AS+" "Female AS+" "Empl Ag" "Empl Manuf")
{res}
{txt}{hline 110}
{txt}                                        (1)             (2)             (3)             (4)             (5)   
{txt}                                    Avg Hrs        Male AS+      Female AS+         Empl Ag      Empl Manuf   
{txt}{hline 110}
{txt}Mean wind potential * time    {res}      -0.0109         -0.0002         -0.0002         -0.0001          0.0003   {txt}
                              {res} {ralign 12:{txt:(}0.0113{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}    {ralign 12:{txt:(}0.0002{txt:)}}    {ralign 12:{txt:(}0.0003{txt:)}}   {txt}
{txt}{hline 110}
{txt}Observations                  {res}         2009            2009            2009            2009            2009   {txt}
{txt}Districts                     {res}          287             287             287             287             287   {txt}
{txt}{hline 110}
{txt}* p<0.10, ** p<0.05, *** p<0.01

{com}. 
. esttab est11 est12 est13 est14 est15 using TableA6.tex, booktabs replace ///
>                 b(%9.4f) stats(N N_clust, labels("Observations" "Districts") fmt(0 0)) ///
>                 varlabels(inter "Mean wind potential * time") varwidth(30) ///
>                 eqlabels(none) noconstant se nonotes unstack legend star(* 0.10 ** 0.05 *** 0.01) ///
>                 mtitles("Avg Hrs" "Male AS+" "Female AS+" "Empl Ag" "Empl Manuf") width(\hsize) 
{res}{txt}(output written to {browse  `"TableA6.tex"'})

{com}. 
.                 
.                 
. ** Close log file
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/AliceZhang/Dropbox/Research_Columbia/Renewables Voting (Urpelainen Zhang)/JOP/UZ_JOP2021_Replication/Analysis/logSTATA/003_balanceTest.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 Nov 2021, 19:49:47
{txt}{.-}
{smcl}
{txt}{sf}{ul off}